A method for obtaining fault classification signals characterizing faulty loops in a multiphase electric energy supply network is described in the Siemens device manual xe2x80x9cDigitaler Abzweigschutz (Digital Feeder Protection) 7SA511 V3.0,xe2x80x9d Order No. C53000-G1100-C98-1, 1995, page 36. In this conventional method, impedance starting occurs in the form of a loop-related starting process. In this process, following the execution of an initial process step for ground fault detection in the case of at least one detected ground fault, the conductor-ground loop, and in the case of no detected ground fault, the conductor-conductor loop, are monitored. A loop is considered started if the corresponding measured impedance vector is within the starting polygon for the particular loop. If several loops are started at the same time, an impedance comparison is made in which only loops whose impedance is no more than 1.5 times the smallest loop impedance are classified as started.
In a method for obtaining fault classification signals characterizing faulty loops described in a relatively old German Patent Application No. P 195 45 267.4, in order to eliminate with a great degree of certainty all of the loops which despite initial starting actually are not faulty, the actual faulty loops are determined through comparison of virtual impedances calculated with respect to the measured conductor-conductor loops, by absolute value and phase, with the impedances determined upon impedance starting if only loops without ground faults are found. If at least one loop with ground fault is found, faultless conductor-ground loops are recognized and eliminated through a comparison of the absolute values of the virtual impedance values formed from the impedance values of the conductor-ground loops detected as faulty with the smallest virtual impedance value formed from the impedance values of the fault-free conductor-ground loops. For further processing of the impedance values of the other, non-eliminated, perceived-as-faulty loops, variously configured test procedures are used in view of the number of simultaneously detected conductor-ground loops. Among such test procedures, the particular test procedure assigned to the particular number of conductor-ground loops detected is conducted.
The dissertation xe2x80x9cEinsatz neuronaler Netze im Distanzschutzxe2x80x9d (Use of Neural Networks in Impedance Protection], pages 71 through 76 by T. Dalstein, published in xe2x80x9cFortschritt-Berichte VDIxe2x80x9d [VDI Progress Reports), Series 21: Elektrotechnik, No. 173, decribes the use of neural networks for the generation of fault classification signals. This network is trained by applying to it sampling values of current and voltage simulated for at least 50,000 malfunctions. The training must be performed individually for the particular installation site in an energy supply system, making manufacturing costs for a protective device equipped with such a neural network extremely high so that it cannot be considered for application in practice. A starting arrangement is assigned to the neural network.
The present invention relates to a method for generating fault classification signals which designate fault loops in a multiphase energy supply system which form in the case of a fault observed from a protective device with a starting arrangement in which a neural network is used which is trained with input values simulating faulty loops, and in which, in the event of faults, measured values derived from currents and voltages of the loops of the energy supply system are applied to the neural network at its inputs for the generation of fault classification signals in order to receive the fault classification signals at its outputs.
An object of the present invention is to further develop a method which can be carried out at a relatively low cost and therefore can be applied in practice.
To achieve this object according to the present invention, a neural network is used which is trained with input variables simulating faulty loops in the form of normalized resistance and reactance variables formed with consideration of the starting characteristic of the starting arrangement. For the purpose of generation of the fault classification signals in the event of a fault, resistance and reactance measured variables of the loops normalized taking into consideration the starting characteristic are applied to the neural network trained in such manner.
A method for obtaining a fault-identifying signal through a neural network arrangement is described in German Patent No. 43 33 257. In this method, normalized voltage values are supplied to the neural arrangement. A fault-identifying signal is generated with which it is possible to distinguish between a short circuit with arcing and a metallic short circuit. In addition, normalization of the voltage values apparently takes place in the conventional manner and not taking into consideration a starter characteristic of normalized resistance and reactance measured variables which in the method according to the invention is absolutely necessary for a suitable training process of the neural network arrangement.
The same also applies with respect to the use of normalized sampling signals with respect to an additional method described in German Patent No. 43 33 260. In this method, in contrast to the above described method and the method according to the present invention, a starting signal can be obtained in a selective protection arrangement.
An impedance protection device which contains a neural network arrangement as an essential component is described in German Patent No. 44 33 406. This arrangement has a separate neural network for each possible fault type on the monitored section of an energy supply network. Assigned to the neural network arrangement is a fault type identification device which is connected to the input of a device for the extraction of features. On the output side, this device is connected to contact devices which correspond in number to the possible fault types. The outputs of all contact devices are brought to a common output at which, in the event of a fault of a certain type in the monitored section, an output signal of the neural network occurs as a result of the corresponding contact device being triggered, such output signal being provided for detection of this specific fault.
A method for the generation of signals which identify the type of a fault with respect to single-pole faults to ground, bipolar faults with ground contact, bipolar faults without ground contact, and three-pole faults with or without ground contact is described in German Patent No. 43 33 258. Thus fault classification signals which identify faulty loops formed in the case of a fault are not generated. In this conventional method, a single neural network with several neurons in its output layer is used which is trained with current and voltage values normalized in a conventional manner so that in the event of a fault of a certain type, a particular neuron of the output layer transmits an output signal.
The book xe2x80x9cIndustrielle Anwendung Neuronaler Netzexe2x80x9d (Industrial Application of Neural Networks) by E. Schoneburg, 1993, pages 51 and 324, decribes a system in which the measurementxe2x80x94in the framework of a gear unit diagnosis with neural networksxe2x80x94of all upshifts of the gear unit is investigated with a neural network in which the various response times and slip times are normalized.
German Patent No. 43 33 259 describes a method for generating a direction signal which indicates the direction of a short-circuit current. The direction signal indicates whether a short circuit occurred in a first or a second direction from the measuring point. A neural network is used for generating the direction signal. The neural network receives neurons on which subtraction values are used. The direction signal is formed using normalized sampling values of the currents. The neural network is trained accordingly. The normalized sampling values are formed in a normalizing component in the conventional manner, taking the specified current value into account.
The periodical xe2x80x9cELEKTRIC,xe2x80x9d 47 (1993) 1, pp. 2-10, describes the use or neural networks in impedance protective devices with which the position of a fault in an energy supply network can be determined. Such devices in general do not begin measuring activity until after a fault classification signal is generated by an assigned starting arrangement in the event of a fault. Such a fault classification signal is not described in the periodical. Nevertheless, in the conventional device, normalized values are processed. The imaginary part of the impedance is normalized to 100% of the line strength.
An advantage of the method according to the present invention is that it can be carried out with a neural network which is uniformly trained for use at different sites of energy supply systems so that the neural network which has been trained once can be xe2x80x9ccopiedxe2x80x9d for different application purposes. This is attributable to the fact that in the method according to the present invention, the training of the neural network does not take place with respect to currents and voltages developing in the event of faults, which simulate faulty loops but rather with normalized resistance and reactance values, simulating faulty loops. In practical application, normalized resistance and reactance measured variables must be formed in the framework of the method according to the present invention with respect to the particular starting characteristic of the starting arrangement from the particular resistance and reactance measured variables determined, after which the normalized resistance and reactance measured variables are applied to the neural network. By taking into consideration the particular starting characteristic or the particular starting polygon during the formation of the normalized resistance and reactance measured variables of the loops, an adjustment is thus made, in the method according to the present invention, to differing situations at the particular installation site in the particular energy supply system. A further advantage of the method according to the present invention is that on the basis of the use of the appropriately trained neural network, the advantageous possibility of being able to unambiguously distinguish faulty loops from loops which are not actually faulty even in a difficult borderline situation is provided.
In an alternative advantageous embodiment of the method according to the present invention, a neural network is used which has been trained with input variables simulating faulty loops in the form of resistance and reactance variables of the loops and the position of these variables in relation to the classifying signals which describe the starting characteristic. Resistance and reactance variables of the loops and discrimination signals describing the position of these variables with respect to the particular starting characteristic signals are applied to the neural network trained in this manner for the purpose of generating fault classification signals in the event of a fault.
This embodiment of the method according to the present invention is also based on the neural network being trained using input variables which have to a certain degree been normalized in that in this embodiment the training of the neural network is accomplished not only using input variables simulating faulty loops in the form of resistance and reactance variables of the loops but rather in addition, taking into consideration classifying signals which describe the position of these variables with respect to the starting characteristic of the starting arrangement. By this means, this embodiment of the method according to the present invention also can be readily employed independently of the particular network conditions at different locations of energy supply systems in that only adaptation to the particular circumstances is made through the discrimination signals generated with respect to the particular starting characteristic of the starting arrangement.